# AFFORD\_stakeholders\_assessment

This code package contains the relevant scripts used to generate the  results described in van de Wiel et al.
(2024), "Stakeholder Engagement for Sustainable Open Research Data Support Services: Insights from Interviews and Surveys in Switzerland".

All the code can be accessed as well as part of a Git repository, available at https://gitlab.uzh.ch/crsuzh/afford\_stakeholder\_assess

Here you'll find a brief description of each script and its use:

* 0-audio\_diarization.ipynb

    This is a Jupyter notebook containing a well-documented, step-by-step tutorial/example on how to transcribe and diarize audio
    files. While not integral to our workflow, it was used to develop the 1-audio\_diarization.py and is included as an explainer
    of our methodology.

    *Use:* launch an instance of JupyterLab (https://jupyterlab.readthedocs.io/en/stable/getting\_started/starting.html) 
    and open the notebook.

* 1-audio\_diarization.py

    This is the Python script we employed to automatically transcribe and diarize audio files resulting from 
    interviews.

    *Use:* from command line, run "python3 1-audio\_diarization.py -h". The help message will tell you all about
    the arguments you can pass to the script. 

* 2-tags\_embedding\_analysis.ipynb

    This is a Jupyter notebook containing all steps we performed to embed sentences resulting from our annotation
    process and leverage these embeddings to generate per-annotation-category clusters of annotation sentences.

    *Use:* launch an instance of JupyterLab and open the notebook.

* 3-tags\_comparisons.ipynb

    This Jupyter notebook contains code to automatically load clustered annotation sentences and compare those tagged
    by different researchers, finding and reporting overlaps.

    *Use:* Launch an instance of JupyterLab and open the notebook.

* 4-survey\_analysis.qmd 

    This Quarto notebook was used to generate results from the responses collected in the survey described in 
    Methods section "Data collection strategy".

    *Use:* open the notebook in RStudio.

* 5-expert\_workshops\_analysis.qmd

    This Quarto notebook was used to generate results from the digitised data stemming from the "workshop" 
    component of interviews described in Methods section "Data collection strategy.

    *Use:* open the notebook in RStudio.

